| Grant number: | 18/06858-0 |
| Support Opportunities: | Regular Research Grants |
| Start date: | May 01, 2019 |
| End date: | April 30, 2020 |
| Field of knowledge: | Engineering - Production Engineering - Operational Research |
| Principal Investigator: | Aneirson Francisco da Silva |
| Grantee: | Aneirson Francisco da Silva |
| Host Institution: | Faculdade de Engenharia (FEG). Universidade Estadual Paulista (UNESP). Campus de Guaratinguetá. Guaratinguetá , SP, Brazil |
Abstract
With the increase of competitiveness in general, it has been sought the optimization of processes that depend on many variables to achieve an objective or several objectives. In this research project will be studied experimental problems of the Response Surface Methodology and Mixture Problems, with the inclusion of the uncertainties in productive processes of Biodiesel through the cultivation of microalgae. This approach will also be applied in studies already carried out in the Design of Experiments literature, aiming at comparing of the results of this new approach with the traditional optimization methods, which are the desirability function using the Generalized Reduced Gradient Algorithm. Generally, in the optimization of experimental problems does not take into account the uncertainties inherent to the experiment, as well as uncertainties related to the development of the empirical functions. These uncertainties can affect the quality of the solution obtained by the optimization, that is, the adjustments of the investigated factors, consequently affecting the investigated process, leading to the loss of productivity. This work can be classified as an applied research, having descriptive empirical objectives, since the modeling and optimization aims to understand causal relations that can occur in the reality, favoring the understanding of real processes. The approach is quantitative, with the research method being modeling and simulation. The expected results are the publication of this research in Qualis A1 ENG III journals and in congresses, and the technological contribution to the development of new strategies to optimize experimental problems in the context of uncertainty that are present in the industrial sector. (AU)
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